---
title: "S.Deshboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["linkedin","Twitter","Menu"]
    source_code: embed
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(dplyr)
library(openintro)
library(highcharter)
library(plotly)
library(ggvis)
library(tidyverse)
```
```{r}
df<-read.csv("C:\\Users\\hp\\OneDrive\\Desktop\\R_dataset\\vehicle.csv")
#str(df)
#View(df)
```

```{r}
col <-c("blue","red","darkgreen","dark","darkorange")

```
interactive data visualization
================================

Row
--------------------------------

### car failure Analysis 
```{r}
valueBox(paste("Failure"),
         color = "warning")
```

### car failure Analysis in US

```{r}
valueBox(length(df$State),
         icon = "fa-user")
                
         
```

### **Labour cost **

```{r}
gauge(round(mean(df$lc),
         digits = 2),
         min =0,
         max=350,
         gaugeSectors(success = c(0,150),
         warning = c(150,240),
         danger = c(240,350),
         colors = c("green","yellow","red")))
```

### Massachusetts

```{r}
valueBox(sum(df$State=="MA"),
         icon = 'fa-building')
```

### California

```{r}
valueBox(sum(df$State=="CA"),
         icon = 'fa-building')
```

###  Florida

```{r}
valueBox(sum(df$State=="TX"),
         icon = 'fa-building')
```

Row
------------------

### Failure by State

```{r}
p1<- df %>%
  group_by(State) %>%
  summarise(count =n())%>%
  plot_ly(x = ~State,
          y = ~count,
          color = rainbow(51),
          type = 'bar')%>%
layout(xaxis = list(title = "Failure by State"),
yaxis =list(title ='Count'))
p1
```

### Top States

```{r}
p2<- df %>%
  group_by(State) %>%
  summarise(count =n())%>%
  filter(count >50)%>%
  plot_ly(labels = ~State,
          values = ~count,
          marker=list(color=rainbow(5)))%>%
          add_pie(hole=0.2)%>%
          layout(xaxis = list(zeroline= F,
                              showline=F,
                              showtricklabel=F,
                              showgrid=F))
                yaxis = list(zeroline= F,
                              showline=F,
                              showtricklabel=F,
                              showgrid=F)
p2
```

### FM Vs Milage

```{r}
p3 <-plot_ly(df,
             x = ~fm,
             y= ~Mileage,
             text=paste("FM:" ,df$fm,"Milage",
             df$Mileage),
             type = "bar")%>%
        layout(xaxis=list(title="FM"),
               yaxis=list(title="failure Milage"))
p3
```

### Sctter plot of Month Vs Mileage ->

```{r}
p4 <-plot_ly(df,x= ~fm) %>%
  add_markers(y= ~Mileage,
              text = ~paste("Mileage:", Mileage),
              showlegend =F) %>%
  add_lines(y = ~fitted(loess(Mileage ~fm)),
            name ="loess Smoother",
            color = I("#FFC125"),
            showlegend =T,
            line =list(width =5))%>%
  layout(xaxis = list(title = "month"),
         yaxis =list(title = "Mileage"))
p4
  
```

### Box plot of Top State->

```{r}
df %>%
  group_by(State) %>%
  ggvis(~State,~lc,fill= ~State) %>%
  layer_boxplots()
```

Map
====================

### Map

```{r}
car <- df %>%
  group_by(State) %>%
  summarise(total =n())
car$State <-abbr2state(car$State)

highchart() %>%
          hc_title(text = "car failure in US") %>%
          hc_subtitle(text ="source :vehicle.csv")%>%
          hc_add_series_map(usgeojson,car,
                            name="State",
                            value = "total",
                            joinBy = c("woename","State")) %>%
hc_mapNavigation(enabled =T)
```


Data Table
=============================


```{r}
datatable(df,caption = "failure Data",
          rownames = T,
          filter = "top",
          options = list(pagelength =25))
```

Pivot Table
============================

```{r}
rpivotTable(df,aggregatorName = "count",
            col= "fm",
            rows = "state",
            rendername = "Heatmap")
```

Summary report { data-orientation =columns}
==========================================

Column{data-Width =100}
------------------------------------------

### Max failure month

```{r}
valueBox(max(df$fm),
         icon = "fa user")
```

### Average labour cost
```{r}
valueBox(round(mean(df$lc),
               digits = 2),
         icon = "fa-area-chart")
```

### Average Mileage at failure

```{r}
valueBox(round(mean(df$Mileage),
               digits = 2),
         icon = "fa-area-chart")
```

Column
----------------

Report

* This is report on `r length(df$fm)`car failure,
* Average labor cost was `r mean(df$lc)`.
* Average material cost was `r mean(df$mc)`.
* This report was generate on 'r formate(sys.date()),format="%B %d %y)'

About Report
======================


* Created by : Data Scientist at ABC
* confidential : HIGHLY!